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Article URL: https://hegemon.substack.com/p/orwell-on-ai Comments URL: https://news.ycombinator.com/item?id=49034006 Points: 5 # Comments: 0

US robotics researchers flew to Venezuela with snakebots after getting a call.

The battle we have with AI and something we can do about it. Continue reading on UX Collective »

This essay was written with Barath Raghavan, and originally appeared in The Guardian . Major benchmarks measure what AI can do. None measure whether it does what you mean: the distance between what you ask an AI to do and the unspoken assumptions about how you want the AI to do it. We propose a new metric: the Genie coefficient. There’s often a gap between one person’s request and another’s understanding. Most of the time, we bridge it using general knowledge. For example, if you ask a friend to

Article URL: https://github.com/lordraw77/llmproxy Comments URL: https://news.ycombinator.com/item?id=49033596 Points: 2 # Comments: 0

Someone installed a popular AI assistant on a rented server, switched off the setting that makes it ask permission before running risky commands, and pointed it at Thailand's Ministry of Finance, which runs the country's treasury and tax collection. The agent then worked through the ministry's network on its own, checking hosts for ways to gain root access, hunting through file systems, and

“I think it should stand for artificial idiot,” one 9-year-old says. Here’s why kids of all ages are calling AI “disgusting” and “creepy.”

The British AI Security Institute and the U.S. Center for AI Standards and Innovation tested Moonshot AI's Kimi K3 on offensive cyber tasks. Kimi K3 scored 32 percent on ExploitBench, compared with 76 percent for leading U.S. models, while its safeguards failed to block exploit development or simulated attacks. The gap between its strong general benchmark scores and weaker cyber performance also fits allegations that Moonshot AI distilled Anthropic's models. The article Kimi K3 trails frontier U

Seth Meyers has responded to Elon Musk saying he wants to make a "historically accurate" AI version of "The Odyssey" using Grok.

Article URL: https://bfl.ai/blog/flux-3-mimic Comments URL: https://news.ycombinator.com/item?id=49033127 Points: 317 # Comments: 50

If you’re tired of sponsored and AI-summarized search results, Kagi gives you a curated—and human-centric—way to find what you’re looking for.

Article URL: https://sliplane.io/blog/hetzner-inference Comments URL: https://news.ycombinator.com/item?id=49033087 Points: 144 # Comments: 77

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The newest way to buy enterprise AI agents from OpenAI does not involve buying anything online. OpenAI Presence, announced on July 22, is a managed product delivered through a limited general availability programme, and the company states plainly that it is not yet available as a self-serve product. Deployments are led by OpenAI’s own Forward Deployed Engineers […] The post OpenAI Presence sells enterprise AI agents with engineers attached appeared first on AI News .

<p> AI Receptionist that Answers Calls & WhatsApp 24/7 </p> <p> <a href="https://www.producthunt.com/products/estera?utm_campaign=producthunt-atom-posts-feed&utm_medium=rss-feed&utm_source=producthunt-atom-posts-feed">Discussion</a> | <a href="https://www.producthunt.com/r/p/1205300?app_id=339">Link</a> </p>

Eight security flaws in NodeBB went public on Wednesday, along with the code to exploit them. Aikido Security rates all eight as high severity and says its AI pentest agents found them in a six-hour review of the forum software's source code. Every version before 4.14.0 is affected. NodeBB has fixed them all, and administrators should be on 4.14.2. The simplest one takes a settings change. A

Article URL: https://arxiv.org/abs/2601.04463 Comments URL: https://news.ycombinator.com/item?id=49032350 Points: 2 # Comments: 0

Google's Gemini Spark AI agent is now available for Google AI Pro users in the U.S.

Discover how AI is changing the top-of-funnel SEO playbook. Learn four new strategies to adapt to AI search, build third-party citations, and measure success effectively.

Redis shipped seven security releases on July 23 after researchers published authenticated RCE PoCs for stock Redis 6.2.22, 7.4.9, 8.6.4, and 8.8.0. All four chains require RESTORE. The Streams chains also need EVAL and XGROUP; the 8.8.0 chain needs EVAL and the bundled RedisBloom module. Redis says the underlying memory flaws may lead to remote code execution. Redis 6.2.23, 7.2.15, and 7.4.10

Chipzilla changes PC biz name to reflect belief edge and robots could be as valuable one day

Article URL: https://platform.claude.com/cookbook/ Comments URL: https://news.ycombinator.com/item?id=49031409 Points: 326 # Comments: 163

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At this week’s AI Summit in San Francisco, South Korean President Jae Myung Lee and some of the country’s top business leaders and researchers are meeting with NVIDIA and ecosystem partners to chart Korea’s AI progress. Building on NVIDIA founder and CEO Jensen Huang’s visit to Korea last month, this week’s discussions and announcements advance […]

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arXiv:2607.20452v1 Announce Type: new Abstract: Modern software quality assurance demands intelligent, autonomous systems capable of adaptive decision-making across distributed cloud environments. This paper presents AINTMA (Agentic Intelligent Test Management Architecture), a multi-agent agentic AI system that transforms traditional test management into an autonomous quality intelligence ecosystem. AINTMA deploys six specialized AI agents (Test Discovery, Risk Assessment, Reinforcement Learning

arXiv:2607.20462v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly integrated into clinical workflows, stressing the need for reliable traceability of model-generated output with watermarking. Yet, most watermarks are evaluated on general-purpose benchmarks, leaving domains like medicine, where small token-level perturbations can result in significant semantic changes, underexplored. In this work, we present the first rigorous study of how LLM watermarks affect medical

arXiv:2607.20463v1 Announce Type: new Abstract: This paper presents an AI-driven browser extension that identifies clickbait to help users avoid misleading Internet articles. Moving beyond traditional detection, the application employs a hybrid machine learning architecture that combines transformer-based embeddings with linguistically motivated features and a custom "baitness" score. After evaluating various natural language processing techniques -- from classic vectorizers to large language mo

arXiv:2607.20466v1 Announce Type: new Abstract: Rigorous benchmarks have driven progress in autonomous GPU kernel performance optimization by establishing a shared target to hillclimb on, but no equivalent exists for TPUs. We present JAXBench, a TPU-native benchmark suite for AI-generated kernel optimization on Google Cloud TPUs. JAXBench comprises 50 JAX workloads that are both relevant and provide headroom for optimization. We extract 17 production ML operators from architectures in the public

arXiv:2607.20467v1 Announce Type: new Abstract: While parallel decoding is central to the efficiency of Diffusion Large Language Models (dLLMs), current strategies are often hindered by overly conservative confidence thresholds. These thresholds, necessitated by the Joint Probability Dependence Error (JPDE), result in redundant denoising iterations and suboptimal inference speeds. To overcome this, we propose DC-Leap, a training-free framework that enables reliable acceleration of dLLMs in the m

arXiv:2607.20468v1 Announce Type: new Abstract: AI agents are increasingly used to automate research and development tasks, yet existing benchmarks typically evaluate them on prescribed workflows or narrow action spaces. Even nominally open-ended tasks can often be solved by retrieving a well-known recipe and tuning a few hyperparameters, making it unclear whether strong results reflect genuine optimization or memorized solutions. We introduce InferenceBench, where an agent must deploy an OpenAI

arXiv:2607.20469v1 Announce Type: new Abstract: Large language models (LLMs) handle many tasks with one set of parameters, but under KV-cached inference it is unclear what task-general structure, if any, is used at decode time rather than during prefill. We propose DecodeShare, a protocol that identifies a low-dimensional subspace consistently shared across tasks in decode-time hidden states, and then tests its causal role by removing that subspace only during decoding. In our experiments, distu

arXiv:2607.20470v1 Announce Type: new Abstract: Enhancing the task-specific capabilities of Large Language Models (LLMs) primarily requires substantial instruction-tuning datasets. However, the sheer volume of such data imposes a considerable annotation cost, and a lack of optimization methods for tailoring LLMs to specific tasks. To address the above issues, we propose a \textbf{Plan}ning framework for constructing \textbf{E}xtractive-based LLMs called \textbf{PlanE}, which includes data decomp

arXiv:2607.20471v1 Announce Type: new Abstract: Personalization, the act of varying a message to induce action from a specific receiver while keeping sender, channel, and time fixed, has a long tradition in psychology and marketing as a two-party problem in which sender and receiver have independent objectives. Large language models remove the bounded-inventory constraint of classical retrieval-and-ranking approaches by generating a continuum of message variants conditioned on inferred receiver

arXiv:2607.20472v1 Announce Type: new Abstract: When a user asks a language model something harmful, is it a genuine attack or a misunderstood but well-meaning question? This ambiguity is one of the central challenges of LLM safety. A model that assumes the worst harms legitimate users; one that assumes the best is easily exploited. The problem is compounded in multi-turn dialogue, where an attacker's true intent may only reveal itself gradually across many exchanges, yet existing safety framewo

arXiv:2607.20473v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly released as open-weight models with safeguards against harmful requests. Nevertheless, sentence completion remains vulnerable to incomplete harmful prompts. In this work, we formalize this phenomenon as incomplete prompt jailbreaks (IPJ) and provide a systematic empirical characterization of when and how incomplete prompts elicit harmful continuations. We analyze diverse attractor types associated with

arXiv:2607.20474v1 Announce Type: new Abstract: Natural language interfaces can greatly benefit the accessibility and usability of optimization modeling, and recent advances in large language models (LLMs) show promise in automatically translating textual problem descriptions into executable solver formulations. However, a key challenge for existing approaches is to ensure that the inferred formulation correctly implements the intended task, even if it may execute without errors. We introduce Ve

arXiv:2607.20475v1 Announce Type: new Abstract: Sampling in LLM inference comprises a combinatorial set of logit processing, token selection, and verification operations for speculative decoding. However, existing implementations either accelerate only subsets of this pipeline, rely on multiple kernel launches, or assume homogeneous sampling behavior across a batch, limiting support for dynamic serving workloads and preventing efficient CUDA Graph execution. We present $\textbf{SonicSampler}$, a